step6_plots_classification_barplot_generation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#@authors  - abhimanyu,roshan,abhijeet\n",
    "# =============================================================\n",
    "# This code generated a bar plot showing the plots classified in various exponentially misfit or fit classes \n",
    "#\n",
    "#=================================================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ExponentialPattern_type</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Data_misfit</td>\n",
       "      <td>1039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Exponential_ID_col</td>\n",
       "      <td>545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Exponential_Used</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Linear</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Dates_on_2_axis</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Falling</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ExponentialPattern_type  number\n",
       "0             Data_misfit    1039\n",
       "1      Exponential_ID_col     545\n",
       "2        Exponential_Used     177\n",
       "3                  Linear     270\n",
       "4         Dates_on_2_axis      21\n",
       "5                 Falling      11"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.read_csv('pattern_data.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data['ExponentialPattern_type']\n",
    "y = data['number']\n",
    "plt.barh(x, y)\n",
    "\n",
    "for index, value in enumerate(y):\n",
    "    plt.text(value, index, str(value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data.plot.bar(x='ExponentialPattern_type', y='number', rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}